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Official repository with code from VQDM: Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization paper.

Usage

Dependencies

Install packages from requirements.txt:

pip install -r requirements.txt

How to quantize a model with VQDM

Please see examples of quantization, finetuning, metric calculation and image_generation scripts in the repository. For quantization - quantization.sh For finetuning - finetuning_quantization.sh For image generation on a set of prompts - eval_image_generation.sh For metric calculation - metric_calculation.sh

Main CLI arguments:
- `CUDA_VISIBLE_DEVICES` - by default, the code will use all available GPUs. If you want to use specific GPUs (or one GPU), set up this variable.
- `--calibration/finetune/evaluation nsamples` - the number of calibration/finetune/evaluation prompts
- `--num_codebooks` - number of codebooks per layer
- `--nbits_per_codebook` - each codebook will contain 2 ** nbits_per_codebook vectors
- `--in_group_size` - how many weights are quantized together (aka "g" in the arXiv paper)
- `--finetune_batch_size` - (for fine-tuning only) the total number of sequences used for each optimization step
- `--local_batch_size` - when accumulating finetune_batch_size, process this many samples per GPU per forward pass (affects GPU RAM usage)
- `--relative_mse_tolerance`- (for initial calibration) - stop training when (current_epoch_mse / previous_epoch_mse) > (1 - relative_mse_tolerance)
- `--save` -- path to save/load quantized model. (see also: `--load`)
- `--wandb` - if this parameter is set, the code will log results to wandb

There are additional hyperparameters aviailable. Run `python main.py and main_text2image.py  --help` for more details on command line arguments.

The code is based on Extreme Compression of Large Language Models via Additive Quantization

BibTeX

@misc{egiazarian2024accuratecompressiontexttoimagediffusion,
      title={Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization}, 
      author={Vage Egiazarian and Denis Kuznedelev and Anton Voronov and Ruslan Svirschevski and Michael Goin and Daniil Pavlov and Dan Alistarh and Dmitry Baranchuk},
      year={2024},
      eprint={2409.00492},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.00492}, 
}